I did a quick test with my MCTS chess engine wth two different
implementations.
A standard MCTS with averaging, and MCTS with alpha-beta rollouts. The
result is like a 600 elo difference
Finished game 44 (scorpio-pmcts vs scorpio-mcts): 1/2-1/2 {Draw by 3-fold
repetition}
Score of scorpio-mcts vs scorpio-pmcts: 41 - 1 - 2 [0.955] 44
Elo difference: 528.89 +/- nan
scorpio-mcts uses alpha-beta rollouts
scorpio-pmcts is "pure" mcts with averaging and UCB formula.
Daniel
On Tue, Mar 6, 2018 at 11:46 AM, Dan <dshawul at gmail.com> wrote:
> I am pretty sure it is an MCTS problem and I suspect not something that
> could be easily solved with a policy network (could be wrong hree). My
> opinon is that DCNN is not
> a miracle worker (as somebody already mentioned here) and it is going to
> fail resolving tactics. I would be more than happy with it if it has same
> power as a qsearch to be honest.
>> Search traps are the major problem with games like Chess, and what makes
> transitioning the success of DCNN from Go to Chess non trivial.
> The following paper discusses shallow traps that are prevalent in chess. (
>https://www.aaai.org/ocs/index.php/ICAPS/ICAPS10/paper/download/1458/1571> )
> They mention traps make MCTS very inefficient. Even if the MCTS is given
> 50x more time is needed by an exhaustive minimax tree, it could fail to
> find a level-5 or level-7 trap.
> It will spend, f.i, 95% of its time searching an asymetric tree of depth >
> 7 when a shallow trap of depth-7 exists, thus, missing to find the level-7
> trap.
> This is very hard to solve even if you have unlimited power.
>> The plain MCTS as used by AlphaZero is the most ill-suited MCTS version in
> my opinion and i have hard a hard time seeing how it can be competitive
> with Stockfish tactically.
>> My MCTS chess engine with AlphaZero like MCTS was averaging was missing a
> lot of tactics. I don't use policy or eval networks but qsearch() for eval,
> and the policy is basically
> choosing which ever moves leads to a higher eval.
>> a) My first improvement to the MCTS is to use minimax backups instead of
> averaging. This was an improvmenet but not something that would solve the
> traps
>> b) My second improvment is to use alphabeta rollouts. This is a rollouts
> version that can do nullmove and LMR etc... This is a huge improvment and
> none of the MCTS
> versons can match it. More on alpha-beta rollouts here (
>https://www.microsoft.com/en-us/research/wp-content/> uploads/2014/11/huang_rollout.pdf )
>> So AlphaZero used none of the above improvements and yet it seems to be
> tactically strong. Leela-Zero suffered from tactical falls left and right
> too as I expected.
>> So the only explanation left is the policy network able to avoid traps
> which I find hard to believe it can identify more than a qsearch level
> tactics.
>> All I am saying is that my experience (as well as many others) with MCTS
> for tactical dominated games is bad, and there must be some breakthrough in
> that regard in AlphaZero
> for it to be able to compete with Stockfish on a tactical level.
>> I am curious how Remi's attempt at Shogi using AlphaZero's method will
> turnout.
>> regards,
> Daniel
>>>>>>>>> On Tue, Mar 6, 2018 at 9:41 AM, Brian Sheppard via Computer-go <
>computer-go at computer-go.org> wrote:
>>> Training on Stockfish games is guaranteed to produce a blunder-fest,
>> because there are no blunders in the training set and therefore the policy
>> network never learns how to refute blunders.
>>>>>>>> This is not a flaw in MCTS, but rather in the policy network. MCTS will
>> eventually search every move infinitely often, producing asymptotically
>> optimal play. But if the policy network does not provide the guidance
>> necessary to rapidly refute the blunders that occur in the search, then
>> convergence of MCTS to optimal play will be very slow.
>>>>>>>> It is necessary for the network to train on self-play games using MCTS.
>> For instance, the AGZ approach samples next states during training games by
>> sampling from the distribution of visits in the search. Specifically: not
>> by choosing the most-visited play!
>>>>>>>> You see how this policy trains both search and evaluation to be
>> internally consistent? The policy head is trained to refute the bad moves
>> that will come up in search, and the value head is trained to the value
>> observed by the full tree.
>>>>>>>> *From:* Computer-go [mailto:computer-go-bounces at computer-go.org] *On
>> Behalf Of *Dan
>> *Sent:* Monday, March 5, 2018 4:55 AM
>> *To:* computer-go at computer-go.org>> *Subject:* Re: [Computer-go] 9x9 is last frontier?
>>>>>>>> Actually prior to this it was trained with hundreds of thousands of
>> stockfish games and didn’t do well on tactics (the games were actually a
>> blunder fest). I believe this is a problem of the MCTS used and not due to
>> for lack of training.
>>>>>>>> Go is a strategic game so that is different from chess that is full of
>> traps.
>>>> I m not surprised Lela zero did well in go.
>>>>>>>> On Mon, Mar 5, 2018 at 2:16 AM Gian-Carlo Pascutto <gcp at sjeng.org> wrote:
>>>> On 02-03-18 17:07, Dan wrote:
>> > Leela-chess is not performing well enough
>>>> I don't understand how one can say that given that they started with the
>> random network last week only and a few clients. Of course it's bad!
>> That doesn't say anything about the approach.
>>>> Leela Zero has gotten strong but it has been learning for *months* with
>> ~400 people. It also took a while to get to 30 kyu.
>>>> --
>> GCP
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